Miami Personal Injury Lawyers AI Visibility Benchmark
A 40-firm benchmark examining AI Visibility Potential and the gap between discoverability and citation-ready first-party evidence.
VIEW FULL RESEARCH →AISearch.wiki Research Library
AI search visibility, measured as evidence.
This library collects AISearch.wiki market benchmarks, methodology, visibility-potential studies and observed AI-search research. The objective is simple: document what can be measured, separate it from what cannot, and make the evidence useful for comparing markets, companies and AI-search behavior over time.
Defined cohorts rather than anecdotal examples
Dated observations rather than permanent-ranking claims
Mentions and citations measured separately
Published AISearch.wiki Evidence
Research designed to be inspected, compared and revisited.
Each study should identify the cohort, research date, measurement type and limitations. Visibility-potential studies evaluate public web foundations; observed-visibility studies measure actual dated AI responses. Those are related—but not interchangeable—forms of evidence.
Putnam County Restaurant AI Visibility Benchmark
A 25-restaurant local-market study of first-party web signals that can support AI discovery, entity understanding, answer extraction and citation-worthiness.
VIEW FULL RESEARCH →Florida Restaurant Market Benchmarks
The restaurant series compares the public AI-search foundations of prominent local restaurant cohorts. The same five-dimension framework allows directional comparison while preserving market-specific cohort caveats.
EXPLORE THE SERIES →AI Search Market Index™ Research
Research by market, not just by website.
The Market Index model expands the question from “Can AI understand this site?” to “Who does AI surface when a buyer asks whom to choose?” That allows AISearch.wiki to compare category visibility, source influence and competitive share of voice.
Market cohorts, buyer-intent queries, citation-ready evidence and observed competitive AI visibility.
SEE LEGAL RESEARCH →Local-market benchmarks examining first-party entity strength, extractability, citation signals and recommendation-oriented queries.
SEE PUBLISHED RESTAURANT STUDY →Competitive AI-search presence across service-category prompts, source citations and firm-level Share of Voice.
RUN AN OBSERVED BENCHMARK →Market-level research into which providers are surfaced when customers ask AI whom to hire for a specific service and geography.
MEASURE A LOCAL MARKET →Two Measurement Layers
Potential is not the same thing as observed visibility.
AISearch.wiki deliberately separates the public signals that may support AI discovery and citation from actual appearances inside dated AI responses.
1. AI Visibility Potential
Evaluates public website and entity signals that can help AI systems discover, understand, verify and extract information about a business.
- Discoverability and indexability
- Entity clarity and identity signals
- Citation-strength and attributable evidence
- Answer-extractable structure
- Trust, freshness and responsible-source signals
2. Observed AI Visibility
Runs defined buyer-intent prompts and records what selected AI systems actually return at a particular point in time.
- Whether the focal entity is mentioned
- Whether it is cited or linked
- Which competitors are surfaced
- Which source URLs appear
- Prompt coverage and competitive Share of Voice
AISearch.wiki Methodology
Measure what can actually be observed.
AISearch.wiki does not claim to reverse-engineer proprietary AI ranking systems. Research is designed around defined inputs, public evidence and reproducible observation procedures.
Define the cohort
State who is included, why the entities belong in the comparison and the geographic or category boundaries.
Define the prompts
Use customer-oriented prompts tied to a real category, need, geography or buying decision rather than one cherry-picked query.
Record the evidence
Timestamp provider observations and retain mentions, citations, source URLs, surfaced competitors and response evidence where applicable.
Separate interpretation
Distinguish measured facts from analyst interpretation, and identify important methodological limitations alongside results.
| Metric | What it means | What it does not mean |
|---|---|---|
| Mention | The entity appears in the observed AI response. | It does not necessarily mean the entity was recommended, ranked first or cited. |
| Citation | A source associated with the entity is explicitly linked or cited in the observed response. | A citation is not equivalent to endorsement or recommendation. |
| Prompt Coverage | The share of monitored prompts in which an entity appears. | It is not a universal percentage of all possible AI searches. |
| AI Share of Voice | The entity's share of appearances within a defined cohort, provider set and prompt panel. | It is not a permanent market share or proprietary platform ranking. |
| Visibility Potential | A diagnostic of public web signals that can support AI discovery, understanding and citation. | It does not predict guaranteed placement in ChatGPT, Gemini, Perplexity or Google AI. |
Interpretation & Limitations
AI answers are dynamic. Research should say so.
Observed results can differ by provider, model, query wording, geography, personalization, retrieval state, available public sources and time. AISearch.wiki treats observations as dated evidence—not permanent rankings.
Every observation has a date.
A result measured today may differ later as model behavior, retrieval systems, business information and public sources change.
Different signals answer different questions.
A business can be mentioned without being cited, or a source can be cited without the business being the primary recommendation.
Benchmark scope determines meaning.
Market results should be interpreted within the declared entity cohort, provider set and prompt panel rather than generalized to every possible search.
Research principle: Defined prompts · timestamped observations · mentions · citations · surfaced competitors · source URLs · explicit limitations.
Use the Research
Move from market evidence to your own measurement.
The research library establishes the framework. The free AISearch.wiki tools apply it to an individual website or defined observed AI-search benchmark.
EvaluateRun the AI Visibility Checker to inspect public visibility-potential signals.
DiagnoseUse the Readiness Checker to identify technical and structural barriers.
CompareBenchmark against relevant competitors and buyer-intent prompts.
VerifyRun observed AI searches and retain timestamped evidence.
AI answered the question. Were you in the answer?
Run a dated observed-visibility measurement across supported AI systems.